Skip to content

Latest commit

 

History

History
643 lines (554 loc) · 35.7 KB

RELEASE.md

File metadata and controls

643 lines (554 loc) · 35.7 KB
Raw

Release 1.0.0

Major Features and Improvements

  • XLA (experimental): initial release of XLA, a domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs.
  • TensorFlow Debugger (tfdbg): command-line interface and API.
  • New python 3 docker images added.
  • Made pip packages pypi compliant. TensorFlow can now be installed by pip install tensorflow command.
  • Several python API calls have been changed to resemble NumPy more closely.
  • Android: person detection + tracking demo implementing Scalable Object Detection using Deep Neural Networks.
  • New (experimental) Java API.
  • Add new Android image stylization demo based on "A Learned Representation For Artistic Style", and add YOLO object detector support.

Breaking Changes to the API

To help you upgrade your existing TensorFlow Python code to match the API changes below, we have prepared a conversion script.

  • TensorFlow/models have been moved to a separate github repository.
  • Division and modulus operators (/, //, %) now match Python (flooring) semantics. This applies to tf.div and tf.mod as well. To obtain forced integer truncation based behaviors you can use tf.truncatediv and tf.truncatemod.
  • tf.divide() is now the recommended division function. tf.div() will remain, but its semantics do not respond to Python 3 or from future mechanisms.
  • tf.reverse() now takes indices of axes to be reversed. E.g. tf.reverse(a, [True, False, True]) must now be written as tf.reverse(a, [0, 2]). tf.reverse_v2() will remain until 1.0 final.
  • tf.mul, tf.sub and tf.neg are deprecated in favor of tf.multiply, tf.subtract and tf.negative.
  • tf.pack and tf.unpack are deprecated in favor of tf.stack and tf.unstack.
  • TensorArray.pack and TensorArray.unpack are getting deprecated in favor of TensorArray.stack and TensorArray.unstack.
  • The following Python functions have had their arguments changed to use axis when referring to specific dimensions. We have kept the old keyword arguments for compatibility currently, but we will be removing them well before the final 1.0.
    • tf.argmax: dimension becomes axis
    • tf.argmin: dimension becomes axis
    • tf.count_nonzero: reduction_indices becomes axis
    • tf.expand_dims: dim becomes axis
    • tf.reduce_all: reduction_indices becomes axis
    • tf.reduce_any: reduction_indices becomes axis
    • tf.reduce_join: reduction_indices becomes axis
    • tf.reduce_logsumexp: reduction_indices becomes axis
    • tf.reduce_max: reduction_indices becomes axis
    • tf.reduce_mean: reduction_indices becomes axis
    • tf.reduce_min: reduction_indices becomes axis
    • tf.reduce_prod: reduction_indices becomes axis
    • tf.reduce_sum: reduction_indices becomes axis
    • tf.reverse_sequence: batch_dim becomes batch_axis, seq_dim becomes seq_axis
    • tf.sparse_concat: concat_dim becomes axis
    • tf.sparse_reduce_sum: reduction_axes becomes axis
    • tf.sparse_reduce_sum_sparse: reduction_axes becomes axis
    • tf.sparse_split: split_dim becomes axis
  • tf.listdiff has been renamed to tf.setdiff1d to match NumPy naming.
  • tf.inv has been renamed to be tf.reciprocal (component-wise reciprocal) to avoid confusion with np.inv which is matrix inversion
  • tf.round now uses banker's rounding (round to even) semantics to match NumPy.
  • tf.split now takes arguments in a reversed order and with different keywords. In particular, we now match NumPy order as tf.split(value, num_or_size_splits, axis).
  • tf.sparse_split now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as tf.sparse_split(sp_input, num_split, axis). NOTE: we have temporarily made tf.sparse_split require keyword arguments.
  • tf.concat now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as tf.concat(values, axis, name).
  • tf.image.decode_jpeg by default uses the faster DCT method, sacrificing a little fidelity for improved speed. One can revert to the old behavior by specifying the attribute dct_method='INTEGER_ACCURATE'.
  • tf.complex_abs has been removed from the Python interface. tf.abs supports complex tensors and should be used instead.
  • In the C++ API (in tensorflow/cc), Input, Output, etc. have moved from the tensorflow::ops namespace to tensorflow.
  • Template.var_scope property renamed to .variable_scope
  • SyncReplicasOptimizer is removed and SyncReplicasOptimizerV2 renamed to SyncReplicasOptimizer.
  • tf.zeros_initializer() and tf.ones_initializer() now return a callable that must be called with initializer arguments, in your code replace tf.zeros_initializer with tf.zeros_initializer().
  • SparseTensor.shape has been renamed to SparseTensor.dense_shape. Same for SparseTensorValue.shape.
  • Replace tf.scalar_summary, tf.histogram_summary, tf.audio_summary, tf.image_summary with tf.summary.scalar, tf.summary.histogram, tf.summary.audio, tf.summary.image, respectively. The new summary ops take name rather than tag as their first argument, meaning summary ops now respect TensorFlow name scopes.
  • Replace tf.train.SummaryWriter and tf.train.SummaryWriterCache with tf.summary.FileWriter and tf.summary.FileWriterCache.
  • Removes RegisterShape from public API. Use C++ shape function registration instead.
  • Deprecated _ref dtypes from the python API.
  • In the C++ API (in tensorflow/cc), Input, Output, etc. have moved from the tensorflow::ops namespace to tensorflow.
  • Change arg order for {softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args.

Bug Fixes and Other Changes

  • New op: parallel_stack.
  • Introducing common tf io compression options constants for RecordReader/RecordWriter.
  • Add sparse_column_with_vocabulary_file, to specify a feature column that transform string features to IDs, where the mapping is defined by a vocabulary file.
  • Added index_to_string_table which returns a lookup table that maps indices to strings.
  • Add string_to_index_table, which returns a lookup table that matches strings to indices.
  • Add a ParallelForWithWorkerId function.
  • Add string_to_index_table, which returns a lookup table that matches strings to indices.
  • Support restore session from checkpoint files in v2 in contrib/session_bundle.
  • Added a tf.contrib.image.rotate function for arbitrary angles.
  • Added tf.contrib.framework.filter_variables as a convenience function to filter lists of variables based on regular expressions.
  • make_template() takes an optional custom_getter_ param.
  • Added comment about how existing directories are handled by recursive_create_dir.
  • Added an op for QR factorizations.
  • Divides and mods in Python API now use flooring (Python) semantics.
  • Android: pre-built libs are now built nightly.
  • Android: cmake/gradle build for TensorFlow Inference library under contrib/android/cmake
  • Android: Much more robust Session initialization code.
  • Android: TF stats now exposed directly in demo and log when debug mode is active
  • Android: new/better README.md documentation
  • saved_model is available as tf.saved_model.
  • Empty op is now stateful.
  • Improve speed of scatter_update on the cpu for ASSIGN operations.
  • Change reduce_join to treat reduction_indices in the same way as other reduce_ ops.
  • Move TensorForestEstimator to contrib/tensor_forest.
  • Enable compiler optimizations by default and allow configuration in configure.
  • tf.divide now honors the name field.
  • Make metrics weight broadcasting more strict.
  • Add new queue-like StagingArea and new ops: stage and unstage.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Hu, Abhishek Aggarwal, Adam Michael, Adriano Carmezim, @AfirSraftGarrier, Alexander Novikov, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Hundt, Anish Shah, Anton Loss, @b0noI, @BoyuanJiang, Carl Thomé, Chad Kennedy, Comic Chang, Connor Braa, Daniel N. Lang, Daniel Trebbien, @danielgordon10, Darcy Liu, Darren Garvey, Dmitri Lapin, Eron Wright, Evan Cofer, Fabrizio Milo, Finbarr Timbers, Franck Dernoncourt, Garrett Smith, @guschmue, Hao Wei, Henrik Holst, Huazuo Gao, @Ian, @Issac, Jacob Israel, Jangsoo Park, Jin Kim, Jingtian Peng, John Pope, Kye Bostelmann, Liangliang He, Ling Zhang, Luheng He, Luke Iwanski, @lvli, Michael Basilyan, Mihir Patel, Mikalai Drabovich, Morten Just, @newge, Nick Butlin, Nishant Shukla, Pengfei Ni, Przemyslaw Tredak, @rasbt, @Ronny, Rudolf Rosa, @RustingSword, Sam Abrahams, Sam Putnam, @SeongAhJo, Shi Jiaxin, @skavulya, Steffen MüLler, @TheUSER123, @tiriplicamihai, @vhasanov, Victor Costan, Vit Stepanovs, Wangda Tan, Wenjian Huang, Xingdong Zuo, Yaroslav Bulatov, Yota Toyama, Yuan (Terry) Tang, Yuxin Wu

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.12.0

Major Features and Improvements

  • TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10, Windows 7, and Windows Server 2016). Supported languages include Python (via a pip package) and C++. CUDA 8.0 and cuDNN 5.1 are supported for GPU acceleration. Known limitations include: It is not currently possible to load a custom op library. The GCS and HDFS file systems are not currently supported. The following ops are not currently implemented: Dequantize, QuantizeAndDequantize, QuantizedAvgPool, QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat, QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool, QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape, QuantizeV2, RequantizationRange, and Requantize.
  • Go: Experimental API in Go to create and execute graphs (https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
  • New checkpoint format becomes the default in tf.train.Saver. Old V1 checkpoints continue to be readable; controlled by the write_version argument, tf.train.Saver now by default writes out in the new V2 format. It significantly reduces the peak memory required and latency incurred during restore.
  • Added a new library for library of matrix-free (iterative) solvers for linear equations, linear least-squares, eigenvalues and singular values in tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization, conjugate gradients and CGLS.
  • Added gradients for matrix_solve_ls and self_adjoint_eig.
  • Large cleanup to add second order gradient for ops with C++ gradients and improve existing gradients such that most ops can now be differentiated multiple times.
  • Added a solver for ordinary differential equations, tf.contrib.integrate.odeint.
  • New contrib module for tensors with named axes, tf.contrib.labeled_tensor.
  • Visualization of embeddings in TensorBoard.

Breaking Changes to the API

  • BusAdjacency enum replaced with a protocol buffer DeviceLocality. PCI bus indexing now starts from 1 instead of 0, and bus_id==0 is used where previously BUS_ANY was used.
  • Env::FileExists and FileSystem::FileExists now return a tensorflow::Status instead of a bool. Any callers to this function can be converted to a bool by adding .ok() to the call.
  • The C API type TF_SessionWithGraph has been renamed to TF_Session, indicating its preferred use in language bindings for TensorFlow. What was previously TF_Session has been renamed to TF_DeprecatedSession.
  • Renamed TF_Port to TF_Output in the C API.
  • Removes RegisterShape from public API. Use C++ shape function registration instead. indexing now starts from 1 instead of 0, and bus_id==0 is used where previously BUS_ANY was used.
  • Most RNN cells and RNN functions now use different variable scopes to be consistent with layers (tf.contrib.layers). This means old checkpoints written using this code will not load after this change without providing Saver a list of variable renames. Examples of variable scope changes include RNN -> rnn in tf.nn.rnn, tf.nn.dynamic_rnn and moving from Linear/Matrix -> weights and Linear/Bias -> biases in most RNN cells.
  • Deprecated tf.select op. tf.where should be used instead.
  • SparseTensor.shape has been renamed to SparseTensor.dense_shape. Same for SparseTensorValue.shape.
  • Env::FileExists and FileSystem::FileExists now return a tensorflow::Status instead of a bool. Any callers to this function can be converted to a bool by adding .ok() to the call.
  • C API: Type TF_SessionWithGraph has been renamed to TF_Session, indicating its preferred use in language bindings for TensorFlow. What was previously TF_Session has been renamed to TF_DeprecatedSession.
  • C API: Renamed TF_Port to TF_Output.
  • C API: The caller retains ownership of TF_Tensor objects provided to TF_Run, TF_SessionRun, TF_SetAttrTensor etc.
  • Renamed tf.image.per_image_whitening() to tf.image.per_image_standardization()
  • Move Summary protobuf constructors to tf.summary submodule.
  • Deprecate histogram_summary, audio_summary, scalar_summary, image_summary, merge_summary, and merge_all_summaries.
  • Combined batch_* and regular version of linear algebra and FFT ops. The regular op now handles batches as well. All batch_* Python interfaces were removed.
  • tf.all_variables, tf.VARIABLES and tf.initialize_all_variables renamed to tf.global_variables, tf.GLOBAL_VARIABLES and tf.global_variables_initializer respectively.
  • tf.zeros_initializer() and tf.ones_initializer() now return a callable that must be called with initializer arguments, in your code replace tf.zeros_initializer with tf.zeros_initializer()

Bug Fixes and Other Changes

  • Use threadsafe version of lgamma function.
  • Fix tf.sqrt handling of negative arguments.
  • Fixed bug causing incorrect number of threads to be used for multi-threaded benchmarks.
  • Performance optimizations for batch_matmul on multi-core CPUs.
  • Improve trace, matrix_set_diag, matrix_diag_part and their gradients to work for rectangular matrices.
  • Support for SVD of complex valued matrices.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

@a7744hsc, Abhi Agg, @admcrae, Adriano Carmezim, Aki Sukegawa, Alex Kendall, Alexander Rosenberg Johansen, @amcrae, Amlan Kar, Andre Simpelo, Andreas Eberle, Andrew Hundt, Arnaud Lenglet, @b0noI, Balachander Ramachandran, Ben Barsdell, Ben Guidarelli, Benjamin Mularczyk, Burness Duan, @c0g, Changming Sun, @chanis, Corey Wharton, Dan J, Daniel Trebbien, Darren Garvey, David Brailovsky, David Jones, Di Zeng, @DjangoPeng, Dr. Kashif Rasul, @drag0, Fabrizio (Misto) Milo, FabríCio Ceschin, @fp, @Ghedeon, @guschmue, Gökçen Eraslan, Haosdent Huang, Haroen Viaene, Harold Cooper, Henrik Holst, @hoangmit, Ivan Ukhov, Javier Dehesa, Jingtian Peng, Jithin Odattu, Joan Pastor, Johan Mathe, Johannes Mayer, Jongwook Choi, Justus Schwabedal, Kai Wolf, Kamil Hryniewicz, Kamran Amini, Karen Brems, Karl Lattimer, @kborer, Ken Shirriff, Kevin Rose, Larissa Laich, Laurent Mazare, Leonard Lee, Liang-Chi Hsieh, Liangliang He, Luke Iwanski, Marek Kolodziej, Moustafa Alzantot, @MrQianjinsi, @nagachika, Neil Han, Nick Meehan, Niels Ole Salscheider, Nikhil Mishra, @nschuc, Ondrej Skopek, OndřEj Filip, @OscarDPan, Pablo Moyano, Przemyslaw Tredak, @qitaishui, @Quarazy, @raix852, Philipp Helo, Sam Abrahams, @SriramRamesh, Till Hoffmann, Tushar Soni, @tvn, @tyfkda, Uwe Schmidt, Victor Villas, Vit Stepanovs, Vladislav Gubarev, @wujingyue, Xuesong Yang, Yi Liu, Yilei Yang, @youyou3, Yuan (Terry) Tang, Yuming Wang, Zafar Takhirov, @zhongyuk, Ziming Dong, @guotong1988

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.11.0

Major Features and Improvements

  • CUDA 8 support.
  • cuDNN 5 support.
  • HDFS Support.
  • Adds Fused LSTM support via cuDNN 5 in tensorflow/contrib/cudnn_rnn.
  • Improved support for NumPy style basic slicing including non-1 strides, ellipses, newaxis, and negative indices. For example complicated expressions like foo[1, 2:4, tf.newaxis, ..., :-3:-1, :] are now supported. In addition we have preliminary (non-broadcasting) support for sliced assignment to variables. In particular one can write var[1:3].assign([1,11,111]).
  • Deprecated tf.op_scope and tf.variable_op_scope in favor of a unified tf.name_scope and tf.variable_scope. The new argument order of tf.variable_scope is incompatible with previous versions.
  • Introducing core/util/tensor_bundle module: a module to efficiently serialize/deserialize tensors to disk. Will be used in TF's new checkpoint format.
  • Added tf.svd for computing the singular value decomposition (SVD) of dense matrices or batches of matrices (CPU only).
  • Added gradients for eigenvalues and eigenvectors computed using self_adjoint_eig or self_adjoint_eigvals.
  • Eliminated batch_* methods for most linear algebra and FFT ops and promoted the non-batch version of the ops to handle batches of matrices.
  • Tracing/timeline support for distributed runtime (no GPU profiler yet).
  • C API gives access to inferred shapes with TF_GraphGetTensorNumDims and TF_GraphGetTensorShape.
  • Shape functions for core ops have moved to C++ via REGISTER_OP(...).SetShapeFn(...). Python shape inference RegisterShape calls use the C++ shape functions with common_shapes.call_cpp_shape_fn. A future release will remove RegisterShape from python.

Bug Fixes and Other Changes

  • Documentation now includes operator overloads on Tensor and Variable.
  • tensorflow.__git_version__ now allows users to identify the version of the code that TensorFlow was compiled with. We also have tensorflow.__git_compiler__ which identifies the compiler used to compile TensorFlow's core.
  • Improved multi-threaded performance of batch_matmul.
  • LSTMCell, BasicLSTMCell, and MultiRNNCell constructors now default to state_is_tuple=True. For a quick fix while transitioning to the new default, simply pass the argument state_is_tuple=False.
  • DeviceFactory's AddDevices and CreateDevices functions now return a Status instead of void.
  • Int32 elements of list(type) arguments are no longer placed in host memory by default. If necessary, a list(type) argument to a kernel can be placed in host memory using a HostMemory annotation.
  • uniform_unit_scaling_initializer() no longer takes a full_shape arg, instead relying on the partition info passed to the initializer function when it's called.
  • The NodeDef protocol message is now defined in its own file node_def.proto instead of graph.proto.
  • ops.NoGradient was renamed ops.NotDifferentiable. ops.NoGradient will be removed soon.
  • dot.h / DotGraph was removed (it was an early analysis tool prior to TensorBoard, no longer that useful). It remains in history should someone find the code useful.
  • re2 / regexp.h was removed from being a public interface of TF. Should users need regular expressions, they should depend on the RE2 library directly rather than via TensorFlow.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abid K, @afshinrahimi, @AidanGG, Ajay Rao, Aki Sukegawa, Alex Rothberg, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Thomas, @Appleholic, Bastiaan Quast, Ben Dilday, Bofu Chen, Brandon Amos, Bryon Gloden, Cissp®, @chanis, Chenyang Liu, Corey Wharton, Daeyun Shin, Daniel Julius Lasiman, Daniel Waterworth, Danijar Hafner, Darren Garvey, Denis Gorbachev, @DjangoPeng, Egor-Krivov, Elia Palme, Eric Platon, Fabrizio Milo, Gaetan Semet, Georg Nebehay, Gu Wang, Gustav Larsson, @haosdent, Harold Cooper, Hw-Zz, @ichuang, Igor Babuschkin, Igor Macedo Quintanilha, Ilya Edrenkin, @ironhead, Jakub Kolodziejczyk, Jennifer Guo, Jihun Choi, Jonas Rauber, Josh Bleecher Snyder, @jpangburn, Jules Gagnon-Marchand, Karen Brems, @kborer, Kirill Bobyrev, Laurent Mazare, Longqi Yang, Malith Yapa, Maniteja Nandana, Martin Englund, Matthias Winkelmann, @mecab, Mu-Ik Jeon, Nand Dalal, Niels Ole Salscheider, Nikhil Mishra, Park Jiin, Pieter De Rijk, @raix852, Ritwik Gupta, Sahil Sharma, Sangheum Hwang, @SergejsRk, Shinichiro Hamaji, Simon Denel, @Steve, @suiyuan2009, Tiago Jorge, Tijmen Tieleman, @tvn, @tyfkda, Wang Yang, Wei-Ting Kuo, Wenjian Huang, Yan Chen, @YenChenLin, Yuan (Terry) Tang, Yuncheng Li, Yunfeng Wang, Zack Polizzi, @zhongzyd, Ziming Dong, @perhapszzy

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.10.0

Major Features and Improvements

  • Added support for C++ shape inference
  • Added graph-construction C API
  • Major revision to the graph-construction C++ API
  • Support makefile build for iOS
  • Added Mac GPU support
  • Full version of TF-Slim available as tf.contrib.slim
  • Added k-Means clustering and WALS matrix factorization

Bug Fixes and Other Changes

  • Allow gradient computation for scalar values.
  • Performance improvements for gRPC
  • Improved support for fp16
  • New high-level ops in tf.contrib.{layers,metrics}
  • New features for TensorBoard, such as shape display, exponential smoothing
  • Faster and more stable Google Cloud Storage (GCS) filesystem support
  • Support for zlib compression and decompression for TFRecordReader and TFRecordWriter
  • Support for reading (animated) GIFs
  • Improved support for SparseTensor
  • Added support for more probability distributions (Dirichlet, Beta, Bernoulli, etc.)
  • Added Python interfaces to reset resource containers.
  • Many bugfixes and performance improvements
  • Many documentation fixes

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Alex Rothberg, Andrew Royer, Austin Marshall, @BlackCoal, Bob Adolf, Brian Diesel, Charles-Emmanuel Dias, @chemelnucfin, Chris Lesniewski, Daeyun Shin, Daniel Rodriguez, Danijar Hafner, Darcy Liu, Kristinn R. Thórisson, Daniel Castro, Dmitry Savintsev, Kashif Rasul, Dylan Paiton, Emmanuel T. Odeke, Ernest Grzybowski, Gavin Sherry, Gideon Dresdner, Gregory King, Harold Cooper, @heinzbeinz, Henry Saputra, Huarong Huo, Huazuo Gao, Igor Babuschkin, Igor Macedo Quintanilha, Ivan Ukhov, James Fysh, Jan Wilken Dörrie, Jihun Choi, Johnny Lim, Jonathan Raiman, Justin Francis, @lilac, Li Yi, Marc Khoury, Marco Marchesi, Max Melnick, Micael Carvalho, @mikowals, Mostafa Gazar, Nico Galoppo, Nishant Agrawal, Petr Janda, Yuncheng Li, @raix852, Robert Rose, @Robin-des-Bois, Rohit Girdhar, Sam Abrahams, satok16, Sergey Kishchenko, Sharkd Tu, @shotat, Siddharth Agrawal, Simon Denel, @sono-bfio, SunYeop Lee, Thijs Vogels, @tobegit3hub, @Undo1, Wang Yang, Wenjian Huang, Yaroslav Bulatov, Yuan Tang, Yunfeng Wang, Ziming Dong

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.9.0

Major Features and Improvements

  • Python 3.5 support and binaries
  • Added iOS support
  • Added support for processing on GPUs on MacOS
  • Added makefile for better cross-platform build support (C API only)
  • fp16 support and improved complex128 support for many ops
  • Higher level functionality in contrib.{layers,losses,metrics,learn}
  • More features to Tensorboard
  • Improved support for string embedding and sparse features
  • The RNN api is finally "official" (see, e.g., tf.nn.dynamic_rnn, tf.nn.rnn, and the classes in tf.nn.rnn_cell).
  • TensorBoard now has an Audio Dashboard, with associated audio summaries.

Bug Fixes and Other Changes

  • Turned on CuDNN Autotune.
  • Added support for using third-party Python optimization algorithms (contrib.opt).
  • Google Cloud Storage filesystem support.
  • HDF5 support
  • Add support for 3d convolutions and pooling.
  • Update gRPC release to 0.14.
  • Eigen version upgrade.
  • Switch to eigen thread pool
  • tf.nn.moments() now accepts a shift argument. Shifting by a good estimate of the mean improves numerical stability. Also changes the behavior of the shift argument to tf.nn.sufficient_statistics().
  • Performance improvements
  • Many bugfixes
  • Many documentation fixes
  • TensorBoard fixes: graphs with only one data point, Nan values, reload button and auto-reload, tooltips in scalar charts, run filtering, stable colors
  • Tensorboard graph visualizer now supports run metadata. Clicking on nodes while viewing a stats for a particular run will show runtime statistics, such as memory or compute usage. Unused nodes will be faded out.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Schumacher, Aidan Dang, Akihiko ITOH, Aki Sukegawa, Arbit Chen, Aziz Alto, Danijar Hafner, Erik Erwitt, Fabrizio Milo, Felix Maximilian Möller, Henry Saputra, Sung Kim, Igor Babuschkin, Jan Zikes, Jeremy Barnes, Jesper Steen Møller, Johannes Mayer, Justin Harris, Kashif Rasul, Kevin Robinson, Loo Rong Jie, Lucas Moura, Łukasz Bieniasz-Krzywiec, Mario Cho, Maxim Grechkin, Michael Heilman, Mostafa Rahmani, Mourad Mourafiq, @ninotoshi, Orion Reblitz-Richardson, Yuncheng Li, @raoqiyu, Robert DiPietro, Sam Abrahams, Sebastian Raschka, Siddharth Agrawal, @snakecharmer1024, Stephen Roller, Sung Kim, SunYeop Lee, Thijs Vogels, Till Hoffmann, Victor Melo, Ville Kallioniemi, Waleed Abdulla, Wenjian Huang, Yaroslav Bulatov, Yeison Rodriguez, Yuan Tang, Yuxin Wu, @zhongzyd, Ziming Dong, Zohar Jackson

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.8.0

Major Features and Improvements

  • Added a distributed runtime using GRPC
  • Move skflow to contrib/learn
  • Better linear optimizer in contrib/linear_optimizer
  • Random forest implementation in contrib/tensor_forest
  • CTC loss and decoders in contrib/ctc
  • Basic support for half data type
  • Better support for loading user ops (see examples in contrib/)
  • Allow use of (non-blocking) Eigen threadpool with TENSORFLOW_USE_EIGEN_THREADPOOL define
  • Add an extension mechanism for adding network file system support
  • TensorBoard displays metadata stats (running time, memory usage and device used) and tensor shapes

Bug Fixes and Other Changes

  • Utility for inspecting checkpoints
  • Basic tracing and timeline support
  • Allow building against cuDNN 5 (not incl. RNN/LSTM support)
  • Added instructions and binaries for ProtoBuf library with fast serialization and without 64MB limit
  • Added special functions
  • bool-strictness: Tensors have to be explicitly compared to None
  • Shape strictness: all fed values must have a shape that is compatible with the tensor they are replacing
  • Exposed tf.while_loop (deprecated control_flow_ops.While)
  • run() now takes RunOptions and RunMetadata, which enable timing stats
  • Fixed lots of potential overflow problems in op kernels
  • Various performance improvements, especially for RNNs and convolutions
  • Many bugfixes
  • Nightly builds, tutorial tests, many test improvements
  • New examples: transfer learning and deepdream ipython notebook
  • Added tutorials, many documentation fixes.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abhinav Upadhyay, Aggelos Avgerinos, Alan Wu, Alexander G. de G. Matthews, Aleksandr Yahnev, @amchercashin, Andy Kitchen, Aurelien Geron, Awni Hannun, @BanditCat, Bas Veeling, Cameron Chen, @cg31, Cheng-Lung Sung, Christopher Bonnett, Dan Becker, Dan Van Boxel, Daniel Golden, Danijar Hafner, Danny Goodman, Dave Decker, David Dao, David Kretch, Dongjoon Hyun, Dustin Dorroh, @e-lin, Eurico Doirado, Erik Erwitt, Fabrizio Milo, @gaohuazuo, Iblis Lin, Igor Babuschkin, Isaac Hodes, Isaac Turner, Iván Vallés, J Yegerlehner, Jack Zhang, James Wexler, Jan Zikes, Jay Young, Jeff Hodges, @jmtatsch, Johnny Lim, Jonas Meinertz Hansen, Kanit Wongsuphasawat, Kashif Rasul, Ken Shirriff, Kenneth Mitchner, Kenta Yonekura, Konrad Magnusson, Konstantin Lopuhin, @lahwran, @lekaha, @liyongsea, Lucas Adams, @makseq, Mandeep Singh, @manipopopo, Mark Amery, Memo Akten, Michael Heilman, Michael Peteuil, Nathan Daly, Nicolas Fauchereau, @ninotoshi, Olav Nymoen, @panmari, @papelita1234, Pedro Lopes, Pranav Sailesh Mani, RJ Ryan, Rob Culliton, Robert DiPietro, @ronrest, Sam Abrahams, Sarath Shekkizhar, Scott Graham, Sebastian Raschka, Sung Kim, Surya Bhupatiraju, Syed Ahmed, Till Hoffmann, @timsl, @urimend, @vesnica, Vlad Frolov, Vlad Zagorodniy, Wei-Ting Kuo, Wenjian Huang, William Dmitri Breaden Madden, Wladimir Schmidt, Yuan Tang, Yuwen Yan, Yuxin Wu, Yuya Kusakabe, @zhongzyd, @znah.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.7.1

Bug Fixes and Other Changes

  • Added gfile.Open and gfile.Copy, used by input_data.py.
  • Fixed Saver bug when MakeDirs tried to create empty directory.
  • GPU Pip wheels are built with cuda 7.5 and cudnn-v4, making them required for the binary releases. Lower versions of cuda/cudnn can be supported by installing from sources and setting the options during ./configure
  • Fix dataset encoding example for Python3 (@danijar)
  • Fix PIP installation by not packaging protobuf as part of wheel, require protobuf 3.0.0b2.
  • Fix Mac pip installation of numpy by requiring pip >= 1.10.1.
  • Improvements and fixes to Docker image.

Release 0.7.0

Major Features and Improvements

  • Allow using any installed Cuda >= 7.0 and cuDNN >= R2, and add support for cuDNN R4
  • Added a contrib/ directory for unsupported or experimental features, including higher level layers module
  • Added an easy way to add and dynamically load user-defined ops
  • Built out a good suite of tests, things should break less!
  • Added MetaGraphDef which makes it easier to save graphs with metadata
  • Added assignments for "Deep Learning with TensorFlow" udacity course

Bug Fixes and Other Changes

  • Added a versioning framework for GraphDefs to ensure compatibility
  • Enforced Python 3 compatibility
  • Internal changes now show up as sensibly separated commits
  • Open-sourced the doc generator
  • Un-fork Eigen
  • Simplified the BUILD files and cleaned up C++ headers
  • TensorFlow can now be used as a submodule in another bazel build
  • New ops (e.g., *fft, *_matrix_solve)
  • Support for more data types in many ops
  • Performance improvements
  • Various bugfixes
  • Documentation fixes and improvements

Breaking Changes to the API

  • AdjustContrast kernel deprecated, new kernel AdjustContrastv2 takes and outputs float only. adjust_contrast now takes all data types.
  • adjust_brightness's delta argument is now always assumed to be in [0,1] (as is the norm for images in floating point formats), independent of the data type of the input image.
  • The image processing ops do not take min and max inputs any more, casting safety is handled by saturate_cast, which makes sure over- and underflows are handled before casting to data types with smaller ranges.
  • For C++ API users: IsLegacyScalar and IsLegacyVector are now gone from TensorShapeUtils since TensorFlow is scalar strict within Google (for example, the shape argument to tf.reshape can't be a scalar anymore). The open source release was already scalar strict, so outside Google IsScalar and IsVector are exact replacements.
  • The following files are being removed from tensorflow/core/public/:
    • env.h -> ../platform/env.h
    • status.h -> ../lib/core/status.h
    • tensor.h -> ../framework/tensor.h
    • tensor_shape.h -> ../framework/tensor_shape.h
    • partial_tensor_shape.h -> ../framework/partial_tensor_shape.h
    • tensorflow_server.h deleted
  • For C++ API users: TensorShape::ShortDebugString has been renamed to DebugString, and the previous DebugString behavior is gone (it was needlessly verbose and produced a confusing empty string for scalars).
  • GraphOptions.skip_common_subexpression_elimination has been removed. All graph optimizer options are now specified via GraphOptions.OptimizerOptions.
  • ASSERT_OK / EXPECT_OK macros conflicted with external projects, so they were renamed TF_ASSERT_OK, TF_EXPECT_OK. The existing macros are currently maintained for short-term compatibility but will be removed.
  • The non-public nn.rnn and the various nn.seq2seq methods now return just the final state instead of the list of all states.
  • tf.scatter_update now no longer guarantees that lexicographically largest index be used for update when duplicate entries exist.
  • tf.image.random_crop(image, [height, width]) is now tf.random_crop(image, [height, width, depth]), and tf.random_crop works for any rank (not just 3-D images). The C++ RandomCrop op has been replaced with pure Python.
  • Renamed tf.test.GetTempDir and tf.test.IsBuiltWithCuda to tf.test.get_temp_dir and tf.test.is_built_with_cuda for PEP-8 compatibility.
  • parse_example's interface has changed, the old interface is accessible in legacy_parse_example (same for related functions).
  • New Variables are not added to the same collection several times even if a list with duplicates is passed to the constructor.
  • The Python API will now properly set the list member of AttrValue in constructed GraphDef messages for empty lists. The serialization of some graphs will change, but the change is both forwards and backwards compatible. It will break tests that compare a generated GraphDef to a golden serialized GraphDef (which is discouraged).

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Akiomi Kamakura, Alex Vig, Alexander Rosenberg Johansen, Andre Cruz, Arun Ahuja, Bart Coppens, Bernardo Pires, Carl Vondrick, Cesar Salgado, Chen Yu, Christian Jauvin, Damien Aymeric, Dan Vanderkam, Denny Britz, Dongjoon Hyun, Eren Güven, Erik Erwitt, Fabrizio Milo, G. Hussain Chinoy, Jim Fleming, Joao Felipe Santos, Jonas Meinertz Hansen, Joshi Rekha, Julian Viereck, Keiji Ariyama, Kenton Lee, Krishna Sankar, Kristina Chodorow, Linchao Zhu, Lukas Krecan, Mark Borgerding, Mark Daoust, Moussa Taifi, Nathan Howell, Naveen Sundar Govindarajulu, Nick Sweeting, Niklas Riekenbrauck, Olivier Grisel, Patrick Christ, Povilas Liubauskas, Rainer Wasserfuhr, Romain Thouvenin, Sagan Bolliger, Sam Abrahams, Taehoon Kim, Timothy J Laurent, Vlad Zavidovych, Yangqing Jia, Yi-Lin Juang, Yuxin Wu, Zachary Lipton, Zero Chen, Alan Wu, @brchiu, @emmjaykay, @jalammar, @Mandar-Shinde, @nsipplswezey, @ninotoshi, @panmari, @prolearner and @rizzomichaelg.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.6.0

Major Features and Improvements

  • Python 3.3+ support via changes to python codebase and ability to specify python version via ./configure.

  • Some improvements to GPU performance and memory usage: convnet benchmarks roughly equivalent with native cudnn v2 performance. Improvements mostly due to moving to 32-bit indices, faster shuffling kernels. More improvements to come in later releases.

Bug Fixes

  • Lots of fixes to documentation and tutorials, many contributed by the public.

  • 271 closed issues on github issues.

Backwards-Incompatible Changes

  • tf.nn.fixed_unigram_candidate_sampler changed its default 'distortion' attribute from 0.0 to 1.0. This was a bug in the original release that is now fixed.

Release 0.5.0

Initial release of TensorFlow.